# Statistical process control withadrian™ aqp

• 468 views

An Introduction into SPC

An Introduction into SPC

### Categories

Uploaded via SlideShare as Microsoft PowerPoint

### Statistics

Likes
0
1
0
Embed Views
26
Views on SlideShare
442
Total Views
468
• Common and Special Causes. Shewhart identified that there are two types of variation, common cause and special cause . Common Cause variation is one which is contained within a natural process which is in a state of statistical control. This variation is inherent in the process and requires fundamental action to reduce it. In the process of a journey with the aim of getting to work this will mean things like waiting time at fixed traffic lights, only fundamental action on the process like changing route or removing the traffic lights will remove the cause of the variation. Special Cause Variation is one which stems from a change which is outside the system or process and is seen as additional variation. In the journey to work example this would include Roadwork's and Breakdowns. In most cases action can be taken to achieve a reduction in the future effect of these causes by better maintenance to avoid the breakdown.
• The histogram is used to show graphically the relative number of occurrences of a range of events. It uses vertical bars…..It plots frequency on the vertical axis against events one after the other on the horizontal axis
• 10
• The Diagram above shows the relationship between Standard Deviation and probability. Most of the time1 we relate to +/- 3 standard deviations, between which we can be sure that 99.73 % of our measured sample will fall between.
• Normal A normal distribution is repeatable and predictable. It defines a stable process The majority of measurements will fall around the mean and occasionally they will fall away from the mean, this is due to natural variation in the process. The natural variation is due to “Common Causes - these are causes common to the process and are always present. Bi - Modal distribution is a process that varies about 2 means. It is possible that 2 processes are being measured or a process has been stopped, reset and continued with the data being continuously collected. The process may also be effected by changes in people or materials. These are all “Special Causes” and can be controlled with relative ease.
• A flat Top distribution is usually due to a process that has drifted. The process could have drifted as a result of tooling wear, temperature change or continuous incremental adjustments or changes to a system. These are Special Causes and action should be taken to identify them. A Skewed distribution is a process where a bias may be present. Faulty measurement process or biased system operators could cause it. There could also be an incremental change in the process under certain conditions. These are also Special Causes.
• Lower Specified Limit = LSL Upper Specified Limit = USL
• Profile: A dedicated, flexible and highly motivated Business Improvements Manager with special interests and achievements in Team Development , and practical experience of Change Management seeking to relocate his career in the United States of America (North Carolina)
• Major Skills and Attributes: Adopted and introduced World Class quality methodologies Proactive Business Excellence Champion , training agent, motivator & mentor Establishing and developing Lean Operations with Visual Control Waste Elimination Extensive practical experience of 5 ‘S’ techniques Skillful implementation of the A.P.P system encompassing QFD, FMEA, and SPC Establishing Process Flow Successfully managed a Risk Mitigation system to minimise cost of non-conformance
• * All 50 States Considered 
• Adrian Beale     USA Address: Date of Birth: 24/09/61 1106 Marital Status: Married Fern Hill Road Health: Excellent Mooresville Driving License: Full/Clean North Carolina Work: +44776337534 USA Mobile: 7049051291

## Statistical process control withadrian™ aqpPresentation Transcript

• SPCSTATISTICAL A mathematical technique tointerpret and organise numerical dataPROCESS A set of linked activities that add valueor produce an item. It will comprise ofthe 5Ms and 1ECONTROL A regulatory mechanism to ensurecorrect characteristic performanceWithAdrian™
• SPC exists because there is variation in the characteristics ofall machines, people, materials, methods, measurements andenvironments.SPC has as its aim “Zero Defect” through the application ofdefect prevention.Statistical Process ControlWithAdrian™
• SPC has as its foundation a philosophy which reduces externalinspection, turning the focus on encouraging individuals tomanage the process to allow their efforts to concentrate oneradicating sources of process variability.SPC seeks to ensure the consistent performance of a processover a long durationStatistical Process ControlWithAdrian™
• VARIATIONCommon Cause is a source of variation that is always present,part of the random variation inherent in theprocess itselfSpecial Cause is a source of variation which is unpredictableor intermittent. It is sometimes called anassignable causeThere are two main types of variability in a process :-WithAdrian™
• DATAVARIABLE This data is a measurement of acharacteristic along a scaleATTRIBUTE This data has only two possibilitiesPass / Fail Yes / NoThere is no measurement. A judgement ismadeWithAdrian™
• BELL SHAPECURVEThe Curve is Symmetricaleither side is a MirrorImage.The highest point of the Bellindicates the mostcommon occurrence. Thisis called the “ArithmeticAverage”ArithmeticAverageXWithAdrian™
• AVERAGESMean: Arithmetic Average of group ofMeasurements. The symbol for a sample isand for a batch isMedian: The middle value in a group of measurementswhen arranged in ascending or descendingorder.Mode: The most frequently occurring number in agroup of measurements.XXWithAdrian™
• SIGMA This indicates an area of deviation from a standardposition.STANDARD DEVIATION( σ ) For sample size data this Greek symbol is used( s ) For batch size data this symbol is usedWithAdrian™
• STANDARD DEVIATIONStandard Deviation Formula :-σ = ΣΣ(n - 1)(n - 1)(x-x)(x-x)22WithAdrian™
• STANDARD DEVIATIONMean99.73 %99.73 %−1σ 3σ2σ−3σ −2σ 1σ+/-+/- 33σσWithAdrian™
• STANDARD DEVIATION99.73 %99.73 %13.5913.59%%13.5913.59%%2σ−2σ34.1334.13%%34.1334.13%%−1σ 1σ 4σ−4σ0.130.13%%0.130.13%%2.142.14%%2.142.14%%3σ−3σ Mean88WithAdrian™
• DISTRIBUTION CURVESNormal DistributionProcess Performs around a CentralMeanRepeatable.Predictable.Binomial DistributionProcess has 2 means caused by:Resetting of Process.Changes in Operator.2 Like processes measured as one.SpreadSpreadXWithAdrian™
• PROCESS CAPABILITYRepeatability: The ability of a measuring device toduplicate measurements when usedseveral times by one individual andmeasuring the identical characteristic.Reproducibility: The difference in the average of themeasurements made by differentpersons using the same or differentmeasuring device when measuringthe identical characteristicWithAdrian™
• DISTRIBUTION CURVESFlat Top - Process Mean hasGradually shifted.Tool Wear.Too Many measurablesin the Process.Skew - Mean of process Biased toone side.Biased Inspection.WithAdrian™
• CCAPABILITYAPABILITY is a ComparisonbetweenActual Performance&A Defined SpecificationWithAdrian™
• PROCESS CAPABILITYProcess Capability is a measure of the variation ofa process and its ability to produce componentsconsistently within specificationsProcess Capability can only be defined when aprocess is in statistical control; this occurs onlywhen special cause variation has been eliminatedWithAdrian™
• PROCESS CAPABILITYCp is the theoretical Capability indexof a process. This index quantifiesthe spread of the process relative tothe specified limits6 σσCp = USL - LSLCp > 1Cp = 1Cp < 1WithAdrian™
• PROCESS CAPABILITYCpk Is the actual capabilityindex of a process. TheCpk index quantifiesboth the spread and thecentring of the process inrelation to the specifiedlimits.UpperToleranceLowerToleranceHigh Cp, High CpkHigh Cp, Very Low CpkHigh Cp, Very Low CpkLow Cp, Low CpkCpk = Minimum value of :-USL - X or X - LSL3 σσ 3 σσWithAdrian™
• PROCESS CAPABILITYIf the process variabilityis wider than theLimits, it is notcapable,and willproduce WAISTWAISTCpk < 1LSL USLWithAdrian™
• PROCESS CAPABILITYIf a process is capable, itis able to producenearly 100% withinthe specified Limits.But this Process isNOT ROBUSTROBUSTCpk = 1LSL USLWithAdrian™
• PROCESS CAPABILITYIf the process variabilityis within the specifiedlimits, the process isvery capable, and willproduce all good parts.This Process hasROBUSTNESSROBUSTNESS!!Cpk > 1LSL USLWithAdrian™
• PROCESS CAPABILITYAccurate:(Cpk) A tight cluster of measurements thatlie within pre-determined limits.Precision:(Cp) A tight cluster of measurements that lieoutside pre-determined limits.WithAdrian™
• Variable Control ChartsVariable DataX Sample AverageIdentify trends within theprocess.R Sample RangeIdentify changes inprocess variation.-5-3-113501234s1 s2 s3 s4 s5 s6 s7 s8 s9 s10XRWithAdrian™
• SIX SIGMA PROCESSLSL 6σ6σ+3σ−3σ XUSLCp = 2Cpk = 1.53.4 ppm RejectsWithAdrian™
• CAPABILITY EXAMPLEPROCESS Cp Index Cpk IndexABCDCp < 1Cp < 1Cp > 2Cp > 2Cpk < 1Cpk = 1Cpk = 1Cpk > 2RESULTLSL USLWithAdrian™
• NNever ending improvement isReflected in anIncreasingIncreasingCpk valueCpk value!!WithAdrian™
• WithAdrian™Before you go…I am on the move from the UK to theUSAandWould love to share some ideas withyou!Now, something about me…
• WithAdrian™I’ma Continuous Improvements Expertand Social Media Pro.
• WithAdrian™I have a Passion for helping peopleand making things happen!
• Achieving…WithAdrian™Growing Global Teams of 600+PeopleslideSHARE over 10,000 viewsGoogle Top RankingsVideo Dominance in TargetedNiches
• Achieving…WithAdrian™£300K Monthly Losses Reduced toZeroBusiness Running Costs Reduced by45%Increased Productivity by 40%£40K Obsolete Material Identified
• WithAdrian™Things I would like to do againLead initiatives, spark creativity, explore insights,cultivate brands, strengthen companies, build teams,encourage others, challenge myself